Amazon Personalize


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This is a short refresher of the 7 AWS machine learning services announced at Re:invent 2018 which will cover:

  • Amazon SageMaker Ground Truth
  • Amazon Forecast
  • Amazon Comprehend Medical
  • Amazon Textract
  • Amazon Personalize
  • Amazon SageMaker RL
  • AWS DeepRacer

Learning Objective

  • It aims to provide an awareness of what each of the ML services is used for and the benefit that they can bring to you within your organization

Intended Audience

  • This course would be beneficial to anyone who is responsible for implementing, managing, and securing machine learning services within AWS


  • You should have a basic understanding of Machine learning concepts and principles to help you understand how each of these services fit into the AWS landscape

Related Training Content

Introduction to Machine Learning on AWS

Applying Machine Learning and AI Services on AWS

AWS Machine Learning - Specialty Certification Preparation


So you've been reviewing your eCommerce store logs and are starting to formulate a picture of shopper disengagement. Shoppers from various demographics just aren't purchasing what you thought they would be. You need to take action to increase product purchasing through online channels and suspect that they eCommerce store needs to be more personalized and targeted towards the end users needs. You're familiar with recommendation, personalization engines, but have neither the budget, nor the time to invest in building one from scratch. What with Amazon Personalize, now you don't need to. 

With Amazon Personalize, you can leverage machine learning tools to generate highly targeted and personalized product listings that help maintain end user engagement and ultimately increase product turnover. Amazon Personalize draws on the many years of knowledge and experience that Amazon has acquired running the eCommerce site. The high level workflow to get up and running with Amazon Personalize is this. 

Firstly, you store your inventory and demographic data in an S3 bucket. Next, use the Amazon Personalize API or JavaScript library to stream end user website activity into your Amazon Personalize endpoint. Next, you use the Amazon Personalize administration console to import your training data and specified schema. Create and train a model know as a solution. Create a campaign, which involves deploying the model created in the previous step. And then testing the campaign. To generate recommendations from the model from known users. Finally, integrate personalization and recommendations back within your website, using the get recommendations and or personalized ranking API operations. 

With Amazon Personalize, when you're configuring your solution, you can either manually select preconfigured recipe or have Amazon Personalize automatically detect and choose the right one for you via the AutoML option. A recipe consists of a recommendation algorithm and other training data required to train the solution. Having successfully integrated your recommendations into your website, continue to stream back end user activity into your solution and this allows you to fine tune and optimize the recommendations by further modeled training. The great thing about this managed service is you don't need to waste time and effort implementing infrastructure, nor setting up machine only recommendation engines or frameworks. Amazon Personalize takes care of this for you. From here on, your energies are solely focused on creating, testing and integrating recommendations and personalization into your website. In quick time, this will result in better end user engagement and growth in sales.

About the Author
Learning Paths

Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.

To date, Stuart has created 150+ courses relating to Cloud reaching over 180,000 students, mostly within the AWS category and with a heavy focus on security and compliance.

Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.

He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.

In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.

Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.